Machine-Learning Optimization and Characterization of a High-Optical-Depth Two-Color Nanofiber Trap

• URL: http://arxiv.org/abs/2606.06798v1
• Date: Fri, 05 Jun 2026 00:57:17 GMT
• Title: Machine-Learning Optimization and Characterization of a High-Optical-Depth Two-Color Nanofiber Trap
• Abstract summary: Optical nanofibers provide a way of coupling quantum information in cold atoms across large distances.<n>Atoms can be trapped close to the surface using a two-color dipole trap.<n>We optimize the number of trapped atoms using a machine learning algorithm and measure the optical density via the transmission.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical nanofibers provide a way of coupling quantum information in cold atoms across large distances, however, this coupling requires atoms to reside close to the nanofiber surface. Atoms can be trapped close to the surface using a two-color dipole trap. Here we present our experimental realization of a two-color dipole trap. We optimize the number of trapped atoms using a machine learning algorithm and measure the optical density via the transmission. We estimate the number of atoms in the trap to be approximately 1400 and the lifetime of the atoms in the trap to be 28 ms. Machine-learning optimization improved the on-resonance optical depth from 0.5 in the initial optimization stage to optical depths exceeding 15.

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